ACUTE LYMPHOBLASTIC LUKEMIA DIAGNOSIS IN BLOOD … · A. N. Sruthi, S. Vinodhini, M. Shyamala Devi Department of Computer Science and Engineering, Veltech Dr. RR & Dr. SR University,

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International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 11, November 2017, pp. 518–525, Article ID: IJCIET_08_11_055

Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=8&IType=11

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

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ACUTE LYMPHOBLASTIC LUKEMIA

DIAGNOSIS IN BLOOD MICROSCOPIC

IMAGES USING LOCAL BINARY PATTERN

AND SUPERVISED CLASSIFIER

A. N. Sruthi, S. Vinodhini, M. Shyamala Devi

Department of Computer Science and Engineering,

Veltech Dr. RR & Dr. SR University, Chennai, India

ABSTRACT

Acute lymphoblastic leukemia (ALL) is a subtype of acute leukemia that is well-

known among adults. The average age of someone with ALL is sixty five years. The

want for automation of leukemia detection arises when you consider that modern-day

methods contain manual exam of the blood smear as the first step towards prognosis.

that is time-ingesting, and its accuracy relies upon at the operator’s capacity. on this

paper, a easy method that mechanically detects and segments ALL in blood smears is

provided. The proposed method differs from others in: 1) the simplicity of the

advanced technique; 2) type of entire blood smear images in place of sub images; and

three) use of those algorithms to section and locate nucleated cells. pc simulation

concerned the following checks: comparing the effect of Haus Orff dimension on the

device earlier than and after the impact of neighborhood binary pattern, evaluating

the overall performance of the proposed algorithms on sub snap shots and entire pics,

and comparing the outcomes of a number of the existing structures with the proposed

machine. eighty microscopic blood photographs were tested, and the proposed

framework controlled to gain ninety eight% accuracy for the localization of the

lymphoblast cells and to separate it from the sub pics and whole pictures.

Keywords: Image Acquisition, Color Features and Color Correlation, Nuclei

Segmentation

Cite this Article: A. N. Sruthi, S. Vinodhini and M. Shyamala Devi, Acute

Lymphoblastic Lukemia Diagnosis in Blood Microscopic Images Using Local Binary

Pattern and Supervised Classifier, International Journal of Civil Engineering and

Technology, 8(11), 2017, pp. 518–525

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=11

A. N. Sruthi, S. Vinodhini and M. Shyamala Devi


1. INTRODUCTION

White blood cells (WBCs) or leukocytes play a chief role inside the diagnosis of different

sicknesses; as a result, extracting information about them is treasured for hematologists. The

time period leukemia comes from the Greek phrase “leukos” which means “white” and “goal”

which means “blood.” It refers to the most cancers of the blood or the bone marrow (wherein

blood cells are produced). Diagnosing leukemia is primarily based on the reality that white

cell count is accelerated with immature blast cells (lymphoid or myeloid), and neutrophils and

platelets are reduced. consequently, hematologists mechanically study blood smear below

microscope for proper identification and type of blast, cells. The presence of the excess

variety of blast cells in peripheral blood is a extensive symptom of leukemia.

ALL is frequently tough to diagnose considering the fact that the precise purpose of ALL

continues to be unknown. further, the signs of the disease are very much like flu or other not

unusual illnesses, consisting of fever, weak spot, tiredness, or aches in bones or joints. If the

described symptoms are present, blood assessments, inclusive of a full blood count number,

renal characteristic and electrolytes, and liver enzyme and blood count, have to be

accomplished. Given that there is no staging for ALL, selecting the type of treatment can

range from chemotherapy, radiation therapy, bone marrow transplant, and organic remedy.

Figure 1 suggests six different pics, three depicting wholesome cells from non-ALL patients

and 3 from ALL patients.

Figure 1 Images from ASH. (a)–(c) Myeloblasts from ALL patients. (d)–(f) Healthy cells from non-

ALL patients.

Otsu segmentation and automated histogram thresholding had been employed to section

WBCs from the blood smear picture. The paintings in hired contour signature to discover the

irregularities in the nucleus boundary. The work in employed selective filtering to phase

leukocytes from the opposite blood additives. This paper is dependent as follows. section II

focuses in detail on the system overview of the proposed version. Sections III and IV display

the photo processing techniques being used to perform enhancement and segmentation.

segment V builds the database of destiny vectors, which might be utilized by the type aspect

of the gadget. Sections VI and VII present the experimental outcomes of the classifier device

based on the capabilities extracted. section VIII consists of conclusions and future paintings

2. PPROCESS OVERVIEW

The gadget proposed guarantees step-by way of-step processing. Figure 2 depicts the gadget

review. The device review gives a detailed depiction of the series of steps which might be to

be followed for green class of acute leukemia. step one entails preprocessing the entire

pictures to conquer any heritage nonuniformity because of irregular illumination.

Acute Lymphoblastic Lukemia Diagnosis in Blood Microscopic Images Using Local Binary Pattern

and Supervised Classifier


Preprocessing also includes shade correlation wherein RGB snap shots are converted to L∗a∗b

colour area images. This step ensures perceptual uniformity. This step is observed by okay-

approach clustering to carry out the nucleus of every cell. Segmentation is followed by

function extraction primarily based on which classification and validation are carried out.

Figure 2 Process Overview

2.1. Preprocessing

CIELAB coloration functions and colour Correlation: The photos generated by way of

digital microscopes are generally in RGB colour area that is tough to segment. In practice, the

blood cells and picture heritage varies greatly with appreciate to colour and intensity. This

will be due to multiple motives along with camera settings, various illuminations, and ageing

stain. With a purpose to make the cellular segmentation sturdy with recognize to these

versions, an adaptive method is used: the RGB enter picture is converted into the CIELAB or,

extra effectively, the CIEL∗a∗b∗ colour area. Because of its perceptual uniformity, L∗a∗b

produces a proportional trade visually for a change of the equal quantity in shade value. This

guarantees that every minute distinction inside the shade cost is observed visually. Being

device unbiased is an delivered gain of the L∗a∗b colour area. Figure 3 presents the end result

of RGB to CIELAB shade conversion and segmentation procedure.

Figure 3 3RGB to CIELAB conversion and segmentation example.



2.2. Nuclei Segmentation

The aim of image segmentation is to extract vital records from an enter photograph. It

performs a key function since the performance of subsequent function extraction and type is

predicated significantly on the precise identification of the myeloblasts. Cluster evaluation

does now not use class labels that tag items with prior identifiers, i.e., elegance labels.

2.3. K-Means Clustering Algorithm

The k-approach set of rules calls for three consumer-distinctive parameters: the quantity of

clusters okay, cluster initialization, and distance metric. Every pixel of an object is assessed

into okay clusters based at the corresponding ∗a and ∗b values inside the L∗a∗b color space.

consequently, each pixel within the L∗a∗b color area is classed into any of the okay clusters

through calculating the Euclidean distance among the pixel and each colour indicator. these

clusters correspond to nucleus (high saturation), background (excessive luminance and low

saturation), and other cells (e. g., erythrocytes and leukocyte cytoplasm)

Figure 4 Feature set developed for the proposed system comprising of shape, color, texture features

2.4. Feature Extraction

Feature extraction in image processing is a technique of redefining a large set of redundant

data into a set of features of reduced dimension. Figure 5 illustrates the given algorithm. It

shows how the nucleus from a noncancer cell is superimposed with a grid of squares to

perform suitable box counting.

Figure 5 Superimposing of the nucleus with a grid of squares for box count measure.

LBP: The concept of local binary pattern (LBP) was introduced for texture classification.

The LBP method has proved to outperform many existing methods, including the linear




discriminant analysis and the principal component analysis. In order to deal with textures at

different scales, the LBP operator was later extended to use neighborhoods of different sizes.

Shape Features with Their Illustrations

Shape features. One of the shape features that has proven to be a good measure for classifying

ALL by their shape is compactness. The shape of the nucleus, according to hematologists, is

an essential feature for discrimination of myeloblasts.

GLCM Features: Texture is defined as a function of the spatial variation in pixel

intensities. The GLCM and associated texture feature calculations are image analysis

techniques. Gray-level pixel distribution can be described by second-order statistics such as

the probability of two pixels having particular gray levels at particular spatial relationships.

This information can be depicted in 2-D gray-level cooccurrence matrices, which can be

computed for various distances and orientations. In order to use information contained in the

GLCM, Haralick defined some statistical measures to extract

Textual characteristic. Some of these features are the following.

• Energy: Also known as uniformity (or angular second moment), it is a measure of

homogeneity of image.

• Contrast: The contrast feature is a difference moment of the regional cooccurrence

matrix and is a measure of the contrast or the amount of local variations present in

an image.

• Entropy: This parameter measures the disorder of an image. When the image is not

texturally uniform, entropy is very large.

• Correlation: The correlation feature is a measure of regional-pattern linear

dependence in the image.

Color features. In addition to the features aforementioned, we have used the following

color-based feature.

1) Cell Energy: Also known as the measure of uniformity, it is the different Lab image

components. We define feature “δ” to be

Where represents the normalized GLCM element for the ith row and

jth column, and represents the ASM.

3. COMPUTER SIMULATION

The selection of a classification method for type is a challenging hassle due to the fact an

appropriate preference given the to be had records can drastically assist improving the

accuracy in credit score scoring practice. There’s a masses of statistical strategies, which

intention at solving binary class duties. In this paper, we use a aid vector system (SVM) for

constructing a selection surface in the function space that bisects the 2 classes, i.e., cancerous

and noncancerous, and maximizes the margin of separation among training of factors.



4. RESULTS AND DISCUSSION

Figure 6 (a) Image selection with pattern (b) Segmentation of pattern

Figure 7 (a) LBP Pattern (b) Classification

Figure 8 Segmentation and Clustering




Figure 9 Clustering Results

5. DATASET ANALYSIS

For each image in the dataset, the classification/position of ALL lymphoblasts is provided by

expert oncologists. Furthermore, we suggest a specific set of figure of merits to be processed

in order to fairly compare different algorithms with the proposed dataset. The images of the

dataset have been captured with an optical laboratory microscope coupled with a Canon

PowerShot G5 camera. All images are in JPG format with 24 bit color depth, resolution 2592

x 1944. The ALL_IDB1 version 1.0 can be used both for testing segmentation capability of

algorithms, as well as the classification systems and image preprocessing methods. This

dataset is composed of 108 images collected during September, 2005. It contains about 39000

blood elements, where the lymphocyte has been labeled by expert oncologists. The images are

taken with different magnifications of the microscope ranging from 300 to 500.

6. CONCLUSION

In this paper, we've proposed a colour-primarily based clustering for segmenting white blood

cells from Acute Lymphoblastic Leukemia images. The technique efficiently combines RGB

and Lab shade space based unique-threshold methods to exploit their complementary

strengths. It consists of three main elements: preprocessing, segmentation, and post

processing. Historical past and red blood cells of the image are extracted through distinct

thresholds in segmentation procedure. The experimental outcomes advised that an overall

segmentation give high accuracy because the first step of computerized white blood cellular

differential system, it indicates a good prospect in further WBC function extraction, ALL

category, and diagnosis.

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Documents

ACUTE LYMPHOBLASTIC LUKEMIA DIAGNOSIS IN BLOOD … · A. N. Sruthi, S. Vinodhini, M. Shyamala Devi Department of Computer Science and Engineering, Veltech Dr. RR & Dr. SR University,